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Dataset results
67 results for “High accuracy”
High accuracy QM data for dimers of water with self, Cl- ion, Na+ ion, Li+ ion, acetate ion, acetamide, and n-methylacetamide
<p>The dataset is the large dataset of quantum-mechanically calculated dimers. QM calculated dimers of water with itself, Cl- ion, Na+ ion, Li+ ion, acetate ion, acetamide, n-methyl-acetamide. This is a subset of dimers that has been used in the published work: <a href="https://doi.org/10.1021/jacs.3c07628">https://doi.org/10.1021/jacs.3c07628</a> . Total count of all the dimers is around 535 thousand configurations. The dimer energetics were calculated with silver standard and using counter-poise correction. The silver standard is MP2/CBS energies calculated and by extrapolating MP2/aug-cc-pvtz->MP2/aug-cc-pvqz + post-mp2 correction CCSD(T)/aug-cc-pvdz-MP2/aug-cc-pvdz. The configurations of dimers are presented in .hin format. Description of the hin format is here (https://wiki.jmol.org/index.php/File_formats/Formats/HIN). All coordinates in the hin files are in the angstrom units. Each hin file has two comment tags that describe energies of this configurations:</p> <p>1) ";conf-ccmp2-EnergyDimer(KCAL/MOL)" - which is the total silver standard energy in kcal/mol units</p> <p>2) ";conf-CCSDt-mp2-correction-added-to-EN-and-DS(KCAL/MOL)" - which is the post-mp2 correction =CCSD(T)/aug-cc-pvdz-MP2/aug-cc-pvdz</p>
Calorimetric data on n-alkanes C15, C16, C17, C18, C19, C20 obtained by high resolution, high accuracy pASC
<p>Three-column files (temperature, specific heat capacity, specific enthalpy)</p>
UWB Ranging and Localization Dataset for "High-Accuracy Ranging and Localization with Ultra-Wideband Communication for Energy-Constrained Devices"
<pre><strong>UWB Ranging and Localization Dataset for "High-Accuracy Ranging and Localization with Ultra-Wideband Communication for Energy-Constrained Devices" </strong> This dataset accompanies the paper "<strong>High-Accuracy Ranging and Localization with Ultra-Wideband Communication for Energy-Constrained Devices</strong>," by <em>L. Flueratoru, S. Wehrli, M. Magno, S. Lohan, D. Niculescu</em>, accepted for publication in the IEEE Internet of Things Journal. Please refer to the paper for more information about analyzing the data. If you find this dataset useful, please consider citing our paper in your work. This dataset is split into two parts: "ranging" and "localization." Both parts contain measurements acquired with 3db Access and Decawave MDEK1001 UWB devices. In the "3db" and "decawave" datasets, when a recording has the same name, it means that the measurements were acquired at the exact same locations with the two types of devices. The "3db" ranging dataset contains, apart from these, more measurements acquired in various LOS and NLOS scenarios. In the directory "images" you can find photos of some of the setups. The "ranging" and "localization" directories both contain a "data" directory which holds the datasets and a "code" directory with Python scripts that show how to read and analyze the data. The 3db Access <em>ranging</em> recordings contain the following data: - True distance - Measured distance - Channel on which the measurements were acquired (can be 6.5, 7, or 7.5 GHz) - Time of arrival as identified by the chipset - Channel impulse response (CIR) - Line-of-sight (LOS)/non-line-of-sight (NLOS) scenario (encoded as 0 and 1, respectively) - If NLOS, the type of NLOS obstruction and its tickness. The Decawave <em>ranging</em> recordings contain the following data: - True distance - Measured distance - Line-of-sight (LOS)/non-line-of-sight (NLOS) scenario (encoded as 0 and 1, respectively) - If NLOS, the type of NLOS obstruction and its tickness. The MDEK kit operates only on the 6.5 GHz channel and cannot output the CIR without further code modifications, which is why this data is not available for the Decawave dataset. The <em>localization</em> dataset includes the following data: - True location as measured by an HTC Vive system - Estimated location using a Gauss-Newton trilateration algorithm (please refer to the paper for more details) - Distance measurements between each anchor and the tag. </pre>
Supplementary material 2 from: Nobile AB, Freitas-Souza D, Ruiz-Ruano FJ, Nobile MLMO, Costa GO, de Lima FP, Camacho JPM, Foresti F, Oliveira C (2019) DNA metabarcoding of Neotropical ichthyoplankton: Enabling high accuracy with lower cost. Metabarcoding and Metagenomics 3: e35060. https://doi.org/10.3897/mbmg.3.35060
: Data type: NGS quality control
Supplementary material 1 from: Nobile AB, Freitas-Souza D, Ruiz-Ruano FJ, Nobile MLMO, Costa GO, de Lima FP, Camacho JPM, Foresti F, Oliveira C (2019) DNA metabarcoding of Neotropical ichthyoplankton: Enabling high accuracy with lower cost. Metabarcoding and Metagenomics 3: e35060. https://doi.org/10.3897/mbmg.3.35060
: Data type: Bioinformatic protocol
Data from: Accuracy of background matching and prey detection: predation by blue tits (Cyanistes caeruleus) indicates intense selection for highly matching prey colour pattern
1. Although background matching decreases prey detectability, resemblance between camouflaged prey and their visual background is seldom perfect. This could be because even a moderate resemblance might provide sufficient protection, and additional adjustment of colour pattern might give little benefit. Alternatively, close resemblance to background may not be attained due to trade-offs or constraints. To understand selection on colour patterns of camouflaged prey and the existence of inaccurate background matching, it is necessary to investigate how detectability of a colour pattern varies with its resemblance to the background. 2. We trained wild-caught blue tits (Cyanistes caeruleus) to search for artificial prey. We manipulated the resemblance of the artificial prey items to the visual backgrounds. 3. For the first half of the twelve repeated prey presentations we found a non-linear relationship between resemblance and detectability, such that for prey that had high background matching, a change in resemblance resulted in a larger change in detectability than an equal change in resemblance did for prey with lower background matching. However, for the second half of the presentations this relationship was linear. Moreover, in a two-patch-type habitat a prey pattern that was a compromise between the two different backgrounds did after few initial presentations equally well as the prey pattern that matched highly one of the backgrounds. 4. Our results indicate an intense selection for close matching in a single background. Yet, in the heterogeneous environment that consisted of two backgrounds the compromise, which only loosely resembled either background, provided good protection. Therefore, we conclude that cryptic colour patterns that bear only a loose resemblance to a given background, and thus represent inaccurate background matching, may be adaptive outcomes.
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models
<p>The zip file contains a synthetic dataset that was used to construct the surrogate model.</p>
A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction
Open the record for dataset details and reuse information.
Data from: High-throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA
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Unexpected high accuracy of landscape genetics inference with convolutional neural networks
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Data from: Accuracy of background matching and prey detection: predation by blue tits (Cyanistes caeruleus) indicates intense selection for highly matching prey colour pattern
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In situ capture of global transcription dynamics and enhancer activity with high accuracy and sensitivity
GEO Series GSE139420. Mus musculus; Homo sapiens. 78 samples. Type: Expression profiling by high throughput sequencing; Other; Genome binding/occupancy profiling by high throughput sequencing.
Enhanced detection of RNA modifications with high-accuracy nanopore RNA basecalling models
GEO Series GSE246151. Xenopus laevis; synthetic construct; Arabidopsis thaliana; Homo sapiens; Mus musculus; Saccharomyces cerevisiae. 70 samples. Type: Other.
A high accuracy of deep learning based CNN architecture: classic, VGGNet, and RestNet50 for Covid-19 image classification
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More Flexible Number Formats for High-Accuracy Simulations - Raw Data
<p>This is the raw data accompanying the thesis "More Flexible Number Formats for High-Accuracy Simulations".</p>
High accuracy calculation and data quality evaluation of ship emissions based on the sniffer method
<p>The uploaded file includes the matlab code used in the study and the actual measurement data set collected.</p>
Evaluation of Diagnostic Accuracy of [68Ga]Ga-PSMA-11 PET/CT in Primary Staging of Intermediate and High Risk Prostatic Cancer in Men Newly Diagnosed
ClinicalTrials.gov study NCT04462926. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Accuracy of the D-Dimer Assay for the Exclusion of Pulmonary Embolism in a High Risk Oncologic Population
ClinicalTrials.gov study NCT00615147. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Diagnostic Accuracy of the "LLIFT", a Novel Non-invasive Biomarker for the Diagnosis of Non Alcoholic Fatty Liver (NAFL) and SteatoHepatitis (NASH) in a Population With High Risk of Metabolic Syndrome
ClinicalTrials.gov study NCT04616664. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High Definition CT Coronary Angiography Accuracy Trial
ClinicalTrials.gov study NCT01946737. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.